Non-kinematic behavioral mapping

ABSTRACT

A system and methodology/processes for non-kinematic/behavioral mapping to a local area abstraction (LAA) includes a technique for populating an LAA wherein human behavior or other non-strictly-kinematic motion may be present.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. §119(e) from U.S.Provisional Patent Application No. 61/182,877 filed Jun. 1, 2009 whichapplication is hereby incorporated herein by reference in its entiretyfor all purposes.

FIELD OF THE INVENTION

The concepts, systems and techniques described herein relate generallyto pedestrian warning systems and more particularly to a method andapparatus for mapping current and future predicted object locations to alocal area abstraction (LAA).

BACKGROUND OF THE INVENTION

As is known in the art, object detection systems for vehicle collisionsystems are known and used to convey the location of objects that couldpotentially collide with a vehicle. One previously known way to describethe location of an obstacle is to describe the latitude and longitude ofthe object. As the number of objects increases, however, the amount ofinformation that must be transmitted to the vehicle also increases. Theincrease in the amount of transmitted information results in aconcomitant increase in the amount of time required by vehicle-mountedsystems to process the information. This results in a delay between thereceipt of the object location information and a collision warning. Thisdelay reduces valuable response time for a driver of the vehicle andthus makes it more difficult for a driver to take evasive action inorder to avoid a collision.

As is also known, for objects moving with rectilinear or curvilinearmotion, given a current position, speed and direction of an object, afuture position of an object can be accurately predicted relativelyeasily using well-known kinematic equations of motion.

The motion of some objects, however, is unpredictable. Human beings, forexample, do not typically adhere to the basic physics of object motionwhich can be described by kinematic equations of motion. Rather, humanbeings in motion are constantly adjusting their speed and directionbased upon sensory input. This non-kinematic motion is very difficult(and in some cases, nearly impossible) to express using simple physicsequations. Thus, predicting future positions for a pedestrian, forexample, can be relatively difficult. In some applications, it is notpossible to yield a single position with an acceptable degree ofconfidence.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the circuits and techniques described herein,may be more fully understood from the following description of thedrawings in which:

FIG. 1 is a top view of an intersection occupancy map (IOM) disposedover an image of a geographic location;

FIGS. 2-2D are a series of pedestrian occupancy maps (POMs);

FIG. 3 is a pedestrian location projected onto a local area abstraction(LAA);

FIG. 4 is a POM overlayed on an LAA;

FIG. 5 is a plurality of probabilities mapped onto an LAA;

FIGS. 6 and 7 is a POM overlayed on an LAA;

FIG. 8 is an enlarged view of a portion of a POM overlayed on a portionof an LAA;

FIG. 9 is a plurality of probabilities mapped onto an LAA;

FIG. 10 is a block diagram of a pedestrian warning system operating inaccordance with the concepts and techniques described in conjunctionwith FIGS. 1-9;

FIG. 11 is an illustration of an object detection system which includesa pedestrian warning system;

FIGS. 12-12B are a series of figures which illustrate track gapping;

FIG. 13 is a diagrammatic view of a scenario in which a pedestrian movestoward a crosswalk;

FIG. 13A is a POM for the scenario of FIG. 13;

FIG. 13B is a diagrammatic view of a pedestrian moving toward acrosswalk;

FIG. 13C is a POM for the scenario of FIG. 13B; and

FIGS. 14 and 14A are diagrams of a collision determination patternvector.

SUMMARY OF THE INVENTION

In view of the above, it has been recognized that there exists a needfor an obstacle detection mapping system which can compactly representthe predicted future location distribution of one or more obstacles, andthen rapidly and compactly transmit the location distributions to avehicle for the purpose of collision detection and avoidance.

It has been found that utilizing a probability distribution mapping,which indicates where a moving object is likely to be at some futurepoint in time, provides a technique to compactly represent thepotentially-complex predicted future location of an obstacle. In oneembodiment, a system and technique utilize a probability distributionmapping which indicates a location at which a pedestrian is likely to beat some future point in time. In one embodiment, for each target, thesystem can pass an amount of data which is approximately one-sixth theamount of data needed using conventional techniques. Thus, using thetechniques described herein it is possible to decrease the amount ofdata transmitted to describe future target locations by approximately84%. It should, of course, be appreciated that a significant benefit(some may say the principle benefit) of the system and techniquesdescribed herein is the ability to reflect complex probabilitydistributions very simply. This results in a compact representation of alocation of the pedestrian and is also desirable technique for providinga reliable, simple, yet useable means for expressing a pedestrianprediction probability distribution.

At least several concepts related to solutions for predicting pedestrianmotion at future times are described herein. First is the use of apedestrian occupancy map (POM) representation of a pedestrianprobability distribution. This pedestrian-centered data map readilypopulates any Intersection Occupancy map (IOM) or other Local AreaAbstraction (LAA). The POM may be used to reflect probabilitydistributions from purely-kinematic motion, to purely random (asobserved) motion, to any degree of non-strictly-kinematic motion inbetween. There is no need to derive an algorithm or equation toapproximate the observed pedestrian behavior, which could provedifficult or impossible due to pedestrian “free will.” The POM approachreadily and accurately reflects pedestrian behavioral habits ortendencies using an aggregate statistical result of prior observedpedestrian behaviors. The surrounding environment of pedestrians isinherently accounted for when forming such aggregate statisticalresults.

Furthermore, the POM-based approach allows for the possibility ofmultiple independent future states separated by a “null space” whereasconventional techniques in the target tracking field solve for a singlepredicted state (position) and variance.

In the time domain, the concepts and techniques described herein allowfor the accurate solution of multiple time-phased predictions as opposedto the single time-state solutions of most classical methods (e.g.Kalman filtering). These multi-time predictions can be chained togetherso as to eliminate the problem of “track gapping” experienced withdiscrete time solutions for high speed objects.

As used herein, the phrase “track gapping” refers to a situation whichcan arise in which a predicted path of an object (e.g. a pedestrian)crosses a path of a vehicle (e.g. a car) at such a time that a systemdoes not provide any indication that a collision (or potentialcollision) between the two objects can or is about to occur. Thus, a“track gap” can be thought of as a “space” (or “gap”) between where acar, for example, is computed at time t_(n) and time t_(n+1). If apedestrian is in the gap, then it is not possible to precisely predict acollision between the car and the pedestrian.

Using conventional techniques to determine the car speed and gap length,it is possible to assign an artificial length to the car so that gapsare eliminated. For example, assuming a car has an actual length offifteen feet, if the gap is determined to be twenty feet given the speedof the vehicle, then by artificially (e.g. mathematically) expanding thelength of the car from fifteen feet to thirty-five feet, the gap iseliminated.

As mentioned above, however, in accordance with the concepts andtechniques of the present disclosure, an accurate solution of multipletime-phased predictions (as opposed to the single time-state solutionsof conventional techniques such as Kalman filtering) which can solve thetrack gap problem is provided. Such multi-time predictions can bechained together so as to eliminate the problem of “track gapping”experienced with discrete time solutions for high speed objects.

The concepts and techniques described herein also easily handlediscrete-event inputs (“controls”) that cause future predicted states tobe non-continuous, again a case for which classical filtering methodsare not ideally suited.

In addition to the POM concept, also described herein is a concept ofthe underlying process/algorithms for computing the exact overlap areafor two convex polygons, in support of “cell POM” allocation to an LAA.

The methods and processes/algorithms described herein populate anIntersection Occupancy Map (IOM) or other local area abstraction (LAA)wherein human behavior or other non-strictly-kinematic motion ispresent. The IOM is a component of a uniquely beneficial interfacedesign methodology for collision avoidance and other vehicular safetyapplications.

The methodology and process for non-kinematic/behavioral mapping to alocal Area abstraction, sometimes referred to as the PedestrianPrediction Logic (PPL), is a comprehensive methodology for populating anintersection occupancy map (IOM) or other local area abstraction (LAA)wherein human behavior or other non-strictly-kinematic motion ispresent. Possible applications include collision avoidance and othervehicular safety applications. This disclosure includes the solutionmethods of related geometry problems for which exact solutions have notpreviously been identified.

In accordance with a further aspect of the disclosure made herein, acomputation-less collision detection methodology and system aredescribed. Such a technique and system may be utilized in an objectwarning system such as a pedestrian warning system. In thecomputation-less collision detection technique, a collisiondetermination pattern vector (CDPV) is provided in conjunction with alocal area abstraction (LAA) of which a pedestrian intersectionoccupancy map (IOM) is one example.

For each greater-than-zero probability in IOM cells (denoted “i”), thevalue of the CDPV cell (denoted CDPV(i*)) is set equal to one (1) andall other CPDV(i*) cell values are set equal to zero. Next, a search or“hash” of CDPV for collisions of interest is performed (e.g.pedestrian-vehicle collision, vehicle-vehicle collisions or othercollisions of interest).

It has thus been recognized that CPDV provides the ability to detectcollisions within intersection occupancy map (IOM) data withoutperforming any collision-specific calculations. This uniquecomputation-less collision detection makes use of the CDPV datastructure. As the IOM is populated with future projected objectlocations, a bit is set in the CDPV based upon each object'sclassification type grouping. In one embodiment, two bits are used foreach grouping and the CDPV reflects if zero, one or more objects of anygrouping is predicted in an IOM cell at a future time (with any non-zeroprobability).

A rapid search (e.g. a “hash”) of the CDPV identifies the cell(s)containing configured collisions of interest (bit patterns) which arethen further checked for collision alert thresholds. By design, certaintypes of collisions will be represented by certain bit patterns. Forexample, a pedestrian-vehicle collision is represented by a specificCDPV bit pattern. A simple configuration change, however, allows thesystem to also check for vehicle-vehicle collisions, or any other typesof collisions.

Thus, while data is being transferred from a POM (or OOM) into an LAA,it is possible to also set CDPV bit patterns to perform computationlesscollision detection.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Described herein is a system and methodology/processes fornon-kinematic/behavioral mapping to a local area abstraction (LAA)including a methodology for populating an LAA wherein human behavior orbehavior of other objects having other non-strictly-kinematic motion maybe present. Before describing such a system and related processes, someintroductory concepts and terminology are explained.

Reference is sometimes made herein to the use of systems and techniquesin vehicular safety applications. One such vehicular safety applicationis referred to as a pedestrian warning system (PWS) which includes apedestrian prediction logic (PPL) system which is an embodiment of anon-kinematic behavioral mapping system and related techniques used topredict pedestrian locations.

It should, of course, be appreciated that a PWS is but one specificexample of a more general object warning system (OWS) and that thegeneral concepts and techniques described herein are not limited to usewithin a PWS.

As used herein the phrase “local area abstraction” or “LAA” refers to anon-location-specific reference frame onto which positions of people andother objects can be projected or “mapped.” This mapping can be for apresent time (represented as “t₀”) or some future or predicted time(represented as “t_(n)”). In an effort to promote clarity in thedescription of the concepts, systems and techniques described herein,reference is sometimes made herein to an LAA represented as a gridhaving a rectangular shape with a particular number of rows and columns(e.g. fifty rows and fifty columns). It should be appreciated that manydifferent types of LAAs are possible, each of which may have differentshapes and sizes including but not limited to circular shapes,triangular shapes, or regular or irregular shapes.

One particular type of LAA used in a pedestrian warning system (and inparticular in a technique to predict pedestrian movement) is referred toas an “intersection occupancy map grid” or IOM grid. In one embodiment,an IOM grid is projected onto or over an image of a geographiclocation—typically an image (e.g. a Google Earth(R) image) wherepedestrians are expected to be found moving amongst other objects suchas vehicles (e.g. cars, trucks, and other motorized or non-motorizedvehicles). It should, of course, be appreciated that an image is notrequired for the system to work (i.e. the techniques described herein donot require the image, per se). Rather, an image facilitates uservisualization. Thus, an LAA overlays a geographic location and may beprojected over an image of the geographic location for uservisualization purposes. Also, again in an effort to promote clarity inthe text and drawings in explaining the concepts, systems and techniquesdescribed herein, reference is sometimes made herein to an IOM gridhaving a rectangular shape with a particular number of rows and columns(e.g. ten rows and thirteen columns). It should, of course, beappreciated that IOM grids having different numbers of rows and columnsmay also be used. Furthermore, IOM grids having shapes other thanrectangular may also be used. The particular shape and size of an IOMgrid to be used in any particular application is selected in accordancewith the needs and requirements of that particular application. Those ofordinary skill in the art will appreciate how to select the particularsize and shape of an IOM grid for a particular application.

Reference is also made herein to a so called “pedestrian occupancy mapgrid” or “POM grid” or more simply a “POM.” As will become apparent fromthe description herein below, a POM provides a representation of apedestrian probability distribution. A POM grid is a pedestrian-centereddata map which populates an IOM grid (or other LAA). The POM grid may beused to reflect probability distributions ranging from purely-kinematicmotion, to purely random (as observed) motion, to any degree ofnon-strictly-kinematic motion in between. The POM may be thought of as amini-map of the future positional probability distribution for anindividual pedestrian or other non-strictly-Kinematic actor. Again, inan effort to promote clarity in the text and drawings in explaining theconcepts, systems and techniques described herein, reference issometimes made herein to a POM grid having a rectangular shape with aparticular number of rows and columns (e.g. five rows and five columns).It should, of course, be appreciated that POM grids having differentnumbers of rows and columns may also be used. Furthermore, POM gridshaving shapes other than rectangular may also be used. The particularshape and size of a POM grid to be used in any particular application isselected in accordance with the needs and requirements of thatparticular application. In general, the size (height, width, area) ofPOM cells and the size of the POM grid (number of rows and columns) areselected to provide meaningful information. If the POM cells areselected to be too large, then little or no meaningful information canbe gained. For example, if the POM cell has a square shape with eachside equal to ten meters, then it would take a walking pedestrian anumber of steps to move from one POM cell to another. Thus, in thisexample, no meaningful location data would be collected for manypedestrian steps, which would result in the POM cell processingtechniques described herein being less effective than they could be. Onthe other hand, the overall POM grid size is preferably limited toreflect physically achievable states.

In general practice, IOM cells may be selected to be in the range ofabout 1-10 times the POM cell size. Thus, if a POM cell size is one-halfmeter, then the IOM cell size is preferably between one-half meter andfive meters. In one embodiment, the POM cell size is typically selectedto be no greater than one-half the size of an IOM cell. Thus, if the IOMcell has a square shape and each side is one meter, then the POM cellwould also preferably be selected having a square shape with each sidebeing no greater than one-half meter. It should be appreciated, however,that it is not necessary that the POM grid shape match the IOM gridshape. It should also be appreciated that the overall POM grid size ispreferably limited to physically achievable states (i.e. the POM gridshould not be defined to contain numerous extraneous cells that wouldalways have a zero probability of occupancy.

It should, however, be understood that IOM grids and POM grids are notlimited to any particular type, size or shape (e.g. the grids as wellsas the cells which make up the grids need not have a square or arectangular shape).

It should also be appreciated that a POM is but one specific example ofa more general object occupancy map (OOM) and that the general conceptsand techniques described herein are not limited to use with a POM.

Accordingly, those of ordinary skill in the art will appreciate that thedescription and processing taking place on pedestrians could equally betaking place on other objects and that portions (either partial orentire portions) of images, grids and cells may be provided havingsquare, rectangular, triangular, polygonal, circular, or ellipticalshape of any size. Likewise, the particular field in which theprocessing systems and techniques described herein may be used includes,but is not limited to, the general area of safety applications,including computerized safety applications and in particular to vehiclesafety applications and even more particularly to pedestrian warningand/or pedestrian safety applications.

Referring now to FIG. 1, an image 12 has an intersection occupancy map(IOM) grid 10 disposed thereover. In the exemplary embodiment of FIG. 1,image 12 corresponds to a portion of a geographic location or region,here corresponding to a portion of a city covering one or more cityblocks. It should be appreciated that IOM grid 10 is positioned andconfigured relative to a geographic region specific roadway (i.e. aspecific longitude and latitude). As mentioned above the image isdesired for visual clarity but not required for the utility of the PWSor any other application. The systems would work equally as well if theIOM grid was not specifically aligned with an image of the primaryroadway of interest. Thus, while image 12 represents one or more cityblocks, in the exemplary embodiment of FIG. 1, IOM grid 10 is disposedover image 12 with respect to a specific roadway such that IOM grid 10has a desired orientation with respect to the specific roadway.

It should also be appreciated that, in an effort to promote clarity inexplaining the concepts described herein, reference is sometimes madeherein to an image 12 or an IOM grid 10 having a particular size orshape or to an IOM grid having a particular grid resolution (i.e. thenumber of grid elements per unit of distance). It should, however, beappreciated that image 12 and/or IOM grid 10 may be provided having anysize or shape or any level of grid resolution. The particular size,shape and/or grid resolution to be used for the image or IOM in anyparticular application will be selected in accordance with the needs ofthe particular application.

Referring now to FIG. 2A a “pedestrian occupancy map” (POM) 14 isutilized to populate an LAA given data parameters (both pedestrian andnon-pedestrian data parameters) including, but not limited to pedestrian(or other object) position, speed, direction, signal phase and timing oftraffic lights, presence and proximity of vehicle (e.g. cars in a roadintersection), time of day, temperature and other weather relatedfactors (e.g. including but not limited to possibility or existence ofprecipitation, wind and rain).

As shown in FIG. 2A, a current pedestrian position 15 is identified onPOM 14. Stated differently, the position 15 of a pedestrian at a timet=t₀ is identified on the POM 14. The POM can be thought of as a “minigrid” (or sometimes referred to as a “mini-LAA”) here having the currentpedestrian (or object) position 15 as its origin (or reference location)as shown in FIG. 2A.

The POM is overlaid onto the IOM at a pedestrian's current position and,in preferred embodiments, the pedestrian location (e.g. a latitude and alongitude) defines the origin of the POM. The particular size, shapeand/or grid resolution to be used for the POM in any particularapplication will be selected in accordance with the needs of theparticular application. A POM is sometimes referred to herein as acurrent-time POM if it shows the current position of a pedestrian. Forexample, POM 14 shows the current position of pedestrian 15 at time t=0and thus POM 14 may be referred to as a current-time POM 14.

It should be appreciated that a system may concurrently utilize aplurality of POMs and that each POM may be different and dependent uponany number of parameters related to the pedestrian or object beingmodeled, including but not limited to positional location, speed,direction, size, etc. Each POM may further be different and dependentupon any number of parameters not related to the pedestrian or objectbeing modeled, including but not limited to signal phase and timing oftraffic lights, presence and proximity of vehicle (e.g. cars in a roadintersection), time of day, temperature and other weather relatedfactors, etc. It should also be appreciated that a collection of POMsmay be constructed and available for use with the methods describedherein, with an individually selected-POM identified by performing a“look-up” or other procedure to determine the best/correct POMrepresentation for a specific pedestrian or actor under specificconditions.

As will be described in detail below, for each POM chosen to model thecurrent or future probable location(s) of a subject pedestrian or actor,the probability distribution of the individual POM is “overlaid onto”and added to the existing LAA, such as an IOM. As will be described indetail below, for the practical implementations, two distinct classes ofPOM have been identified.

Referring now to FIG. 2B, a current-time POM 20 is shown for an LAAdefined to contain probabilities of occupancy. Thus, the current-timePOM is represented in FIG. 2B as a grid or array of numbers with eachnumber corresponding to a probability as to the location of thepedestrian.

Since POM 20 in FIG. 2B represents a current time (t₀), there is a 100%chance (or 100% probability) that the pedestrian “is where he is” at thecurrent time (t₀), and 0% chance that he is anywhere else. Thus, in POM20 of FIG. 2B, the cell located at the intersection of row 2 and columnc (herein denoted “cell 2C” and corresponding to the cell in which thepedestrian is located, contains a value of 1.0. This indicates thatthere is a 100% chance that the pedestrian is located in cell 2C at timet₀. The remaining cells each contain a value of 0.0 indicating thatthere is a 0% chance that the pedestrian is in those cells at time t₀.

Because there exists at least some uncertainty as to the pedestrianlocation at some future time (t_(n)), the probabilities in any futurePOM (i.e. a POM predicting pedestrian location at a future point intime) assume some distribution over the POM cells which totals onehundred percent (100%).

Thus, referring now to FIG. 2C, a future POM 22 (i.e. a POM for wherethe pedestrian 15 (FIG. 2A) will be at some future time (t_(n)))contains a plurality of probability values. The distribution of valuesover the POM cells totals one hundred percent (100%). It should be notedthat in this exemplary embodiment, the pedestrian/object is in motionand thus the POM is also direction-specific as indicated by an arrowlabeled with reference numeral 24. The direction is derived fromsuccessive position observations over time. Most simply, it is thedirection from the last observed position to the current t₀ position,but it may be filtered or smoothed for continuity. Other techniques may,of course, also be used to derive a direction.

In accordance with the concepts described herein, the system/techniquesdescribed herein identify two (2) distinct classes of POM. One class ofPOM is referred to as a so-called “cell POM” or “CPOM” (as shown in FIG.2C), and another class of POM is referred to as a so-called “pin POM” or“PPOM” (as shown in FIG. 2D).

Briefly, a PPOM is formed by assuming that the probabilities containedin the POM occur at some finite number of distinct, infinitesimallysmall points (“pins”) and denoted 25 in FIG. 2D. Each pin 25 is definedto be a specific distance and relative bearing from the POM's referenceposition (or “origin”) and reference heading. Although not required,pins 25 are shown as being located in the center of each cell. Thisserves as a simplifying assumption for the simpler pin-POM method asopposed to the more complex cell-POM method.

A CPOM is formed by assuming that the probabilities contained in the POMoccur within some finite number of distinct, rectangular or other convexpolygon cells/areas, each defined to be a specific size, orientation,distance and bearing from the POM's reference position and referenceheading.

Different techniques are used to assign probabilities to an LAAdepending upon whether a pin POM or a cell POM is used. One techniquefor assigning probabilities to an LAA using a pin POM is described belowin conjunction with FIGS. 4 and 5 and one technique for assigningprobabilities to an LAA using a cell POM is described below inconjunction with FIGS. 6-9. The IOM cell to POM cell size is one factorto consider in deciding whether to use a PPOM or a CPOM technique at anyparticular time or in any particular application and false alarm rate isa factor to consider in selecting IOM cell size. In general, it isdesirable to map a POM proportionally into the LAA and examples aredescribed herein. It should be appreciated that in practical systems,typically the PPOM/CPOM choice may drive the IOM/POM cell sizeselections rather than the other way around. The CPOM is more accurate,but the PPOM is simpler and more computationally efficient.

Referring now to FIG. 3, an LAA 28 having a pedestrian location anddirection marked thereon (as indicated, respectively, by the dotdesignated by reference numeral 30 and the arrow as indicated byreference numeral 32) is used in determining the probability of thepedestrian location at a future point in time. Given pedestrian orobject position 30 and direction 32 within LAA 28, the techniquesdescribed herein can be used for mapping data from either type of POM(i.e. either a cell POM or a pin POM) onto the LAA.

Referring now to FIG. 4, mapping pin POM 26 (FIG. 2D) onto pedestrianlocation 30 (FIG. 3) yields the overlay pictured in FIG. 4. The POMprobability at each “pin” location (as indicated by the dot 25 in thecenter of each cell) is added to the underlying LAA cell probability ofbeing occupied (that is, each LAA cell probability is the sum of all thepin probabilities overlaying that LAA cell—we do show the sums in FIG.5). For a given time t_(n), all LAA probabilities are initialized tozero (0) and then all observed pedestrians/objects are processed, withmultiple pedestrians/objects potentially contributing to any LAA cellprobability at a future time (i.e. there's a probability that two peoplemay end up in the same place). The simplicity of the pin POM approach isthat each probability in the POM maps entirely and exactly into one LAAcell. Thus, using this technique (i.e. mapping the probability in eachPOM cell entirely and exactly into one LAA cell), the resulting LAAprobability distribution for the example pedestrian illustrated in FIG.2A is shown in FIG. 5.

Thus, in the case where a single “pin” (i.e. the center of one cell inPOM 26) falls within a single cell of LAA 28 (FIG. 5), the probabilityof the POM cell is assigned to the LAA cell. For example, as can be seenin FIG. 4, the center of POM cell 1D falls within cell 5I of LAA 28.Thus, with reference now to FIG. 5, LAA cell 5I has a value of 0.02which equals the value of POM cell 1D (this assumes that the initialprobability of LAA cell SI being occupied was zero).

It should, however, be noted that in the case where two “pins” (i.e. thecenters of two cells in POM 26) fall within the same LAA cell, theprobabilities are added and the sum is value associated with the LAAcell. For example, as can be seen in FIG. 4, the centers of cells 1E and2E in POM 26 both fall within cell 5J of LAA 28. Thus, with referencenow to FIG. 5, LAA cell 5J has a value of 0.04 which is determined byadding the values of cells 1E and 2E from POM 26 and assigning the sumto cell 5J of LAA 28 (as above, this assumes that the initialprobability of LAA cell 5J being occupied was zero).

Referring now to FIG. 6, mapping cell POM 22 (aka CPOM 22) onto thepedestrian location 30 (FIG. 3) yields the overlay pictured in FIG. 6.Because the probability for each cell in the POM belongs to the entirecell area (not just a single “pin” point), the probability for any givencell in POM 22 may map into multiple cells of LAA 28, depending upon theareas of overlap.

Referring now to FIG. 7, consider the CPOM cell 4C containing theprobability value of 0.11. POM cell 4C extends over portions of LAAcells 7I, 7J and 8J. Thus, the probability value of POM cell 4C must bedistributed over three LAA cells (i.e. cells 7I, 7J and 8J).

Referring now to FIG. 8, cell 4C from CPOM 22 is shown disposed overcells 7I, 7J and 8J of LAA 28. To accommodate the overlap, the 0.11probability value assigned to POM cell 4C is proportionally distributedto LAA cells 7I, 7J and 8J. The portion of the 0.11 probabilitydistributed to the LAA cell 7J is equal to the ratio of the overlap areaof the POM cell onto the LAA cell (represented by region outlined bydashed line 39 in FIG. 8) divided by the entire area of CPOM cell 4C.For a cell having a rectangular or square shape, the area of each cell,whether it be a POM cell or an LAA cell, is simply the product of thecell height and cell width. In practical systems, LAA cells having asquare shape with height and width equal to 1 m (so LAA cell area equalto 1 meter square) have been used and POM cells having a square shapewith height and width equal to 0.5 m (so POM cell area equal to 0.25square meters). The overlap areas are determined by the processdescribed below. The cells may, of course, be measured in any units.Similar computations are made for LAA cells 7I and 8J.

A resulting distribution of the probability of POM cell 4C to LAA cells7I, 7J, 8J is illustrated in FIG. 9.

One technique for determining the area of the “overlap polygon” (e.g.the region represented by dashed line 39 in FIG. 8) is described below.

As discussed above, the probability assigned to each cell/area of theCPOM is distributed proportionately to the LAA cell(s)/area(s) itoverlaps. For the simple/preferred case in which both the POMcells/areas and the LAA cells/areas are convex polygons, the area ofoverlap between any two convex polygons defines a convex polygon.

The below steps are repeated for each POM convex polygon cell/area.

Each of the n points defining the subject POM convex polygon and mpoints defining the subject LAA convex polygon are added to a list ofpoints, L.

For each of n sides of the POM convex polygon and m sides of the LAAconvex polygon, simple algebra is applied to compute the intersection ofthe POM and LAA (if any). If the two sides intersect, the point ofintersection is added to the list of points, L.

For each point in the list of points L, checks are performed todetermine if the point is on-or-within the POM cell area and/oron-or-within the LAA cell/area, using widely accepted algorithms. If thepoint is not on-or-within BOTH the POM and LAA cells/areas, it isremoved from the list of points, L. At the conclusion of this operation,the list of points L contains only points that are on the perimeter ofthe area of overlap between the POM and LAA; the points being in noparticular order.

If the number of points in list L is 0, 1, or 2, then the area ofoverlap=0.

If the number of points in list L is greater than 2 then the algorithmprocessing continues with the following steps to place the points inlist L into a “clockwise order”.

The first 2 points from list L are copied into clockwise list, C, inpositions #1 and #2.

For each of the p remaining points in list L, the point is “inserted”into successive trial slots in list C (before, between and following theother points in list C) and the resulting list is checked to see if itis counterclockwise at any point, using widely accepted algorithms. Ifthe resulting list is found not to be counterclockwise at any point,then the test point has been properly inserted into list C and the nextpoint in list L is processed. If the resulting list is found to becounterclockwise at any point then the test point has not been properlyinserted into list C and the next candidate slot is tried for the testpoint. A correct insertion into list C will be found for every point inlist L.

The previous step is repeated until all points in list L have beeninserted into list C. The resulting final list C is a list of pointsdefining the area of overlap between the convex POM polygon and convexLAA polygon, sorted in clockwise order.

Using widely accepted algorithms, the area of overlap defined by theclockwise list of points C and the area of the subject convex POM cellare calculated.

The probability of the convex LAA cell/area is increased by theprobability of the subject convex POM cell/area times the ratio of theoverlap area defined by list C divided by the area of the subject convexPOM polygon.

Referring now to FIG. 10 a pedestrian warning system 50 receivesinformation from one or more information sources 56 such as an imagecapture system (e.g. a camera system which may include for example adigital camera or a camera connected to a frame grabber, a video systemwhich, may include a video camera for example, and any other means knownto those of ordinary skill in the art for capturing or otherwiseobtaining images and transmitting or otherwise making image dataavailable to a pedestrian warning system). For example, informationsource 56 may include a network connection which allows the system toreceive image data from a global information network (e.g., an internet)or on intranet or any other type of local or global network. Thus system50 can receive real time or “live” camera images instead of retrievingimages from a database or other storage device. Alternatively stillimages or other information may be fed to system 50 via other means wellknown to those of ordinary skill in the art.

Pedestrian warning system 50 includes a pedestrian detection subsystemsystem 57. Regardless of how information is provided to system 50,pedestrian detection subsystem 57 receives at least some of theinformation and provides information to a pedestrian logic processor 58.Pedestrian logic processor 58 determines locations and possible futurelocations of one or more pedestrians within a region of interest.Pedestrian logic processor 58 determines such information in accordancewith concepts and techniques described herein and provides theinformation to an intersection occupancy map interface 60. Desiredinformation and/or signals and/or, warnings or the like are thenprovided and in some cases the information and/or signals and/or,warnings are provided to both pedestrians and/or to one or more vehicles61 in proximity with the pedestrians.

In one embodiment, a series of POMs/IOMs corresponding to different timeintervals (e.g. two seconds, four seconds, six seconds, eight seconds—sofour maps) are generated. Thus, if a map at t+8 (i.e. a map at 8 secondsinto the future) is examined, it will reveal the probability of wherepedestrians will be in eight seconds.

It should be appreciated that it is possible to have a vast library ofPOMs (i.e. pre-stored POMs having probability values already storedtherein with the probability values being computed for specific factors)or it is possible to compute the POM probability values in real time.

Referring now to FIG. 11, an object detection system 114 includes apedestrian warning system which may be the same as or similar topedestrian warning system 50 described above in conjunction with FIG. 10as well as a plurality of sensors 118 configured to provide coverage ofa predetermined area. The sensors 118 are also configured to detectmovement of objects within the predetermined area. For example, theobject detection system 114 may include a plurality of cameras 118 a, aglobal positioning system 118 b, and other sensors such as radar 118 cand sonar. Each sensor 118 is in communication with the object detectionsystem 114.

The object detection system 114 includes a processing unit 120 (e.g. acomputer processing unit), a path predicting circuit 122 and apedestrian warning system 123. Sensors may detect both stationaryobjects and moving objects as well as objects such as pedestrian 131which may be stationary at one instant in time and moving at a laterpoint in time. If a pedestrian's position, speed and direction areknown, then as described above a probability of a future position ofpedestrian 131 may be determined by a pedestrian logic processor (e.g.such as pedestrian logic processor 58 described above in conjunctionwith FIG. 10). In one embodiment, the pedestrian logic processorutilizes the factors/information provided thereto to access a storagedevice or system (e.g. a database) and look up a probability of thepedestrian's next position using the current factors/information.Processing unit 120 is operable to collect and process sensorinformation. For instance, processing unit 120 may filter corrupt orabnormal sensor information and prevent such information from beingtransmitted to the collision processing circuit 116.

Path predicting circuit 122 processes information gathered by thesensors 118 so as to predict the path of the detected objects within thepredetermined area. Object detection system 114 may further include aplotting circuit 124. Plotting circuit 124 plots the predicted locationof the detected objects. The object detection system 114 may be housedlocally within the predetermined area or may be remote.

Processing unit 120 may also be housed locally within the objectdetection system 114 so as to receive the information from sensors 118on site. The information from the sensors 118 may be processed using thepath predicting circuit 122 and may be further plotted onto a map usingthe plotting circuit 124. Alternatively, the object detection system 14may be remote from the predetermined area. As described above, thecamera 118 a and other sensors 118 may be used to provide coverage for apredetermined area and to detect objects in the area. These sensors 118are in communication with the remotely located object detection system114. The object detection system 114 processes the sensor informationand transmits the processed information to the collision processingcircuit 116 for processing.

The data transferring system 110 includes at least one cycle of data126. Each cycle of data 126 may include a transmission of staticinformation 128 relating to the environment of a predetermined area, andsubsequent transmissions of dynamic information 130 relating to themovement of detected objects such as pedestrians 131 within thepredetermined area. In one embodiment, the transmission of staticinformation includes a map definition 128, and the subsequenttransmissions include a series of overlays 130.

The map definition 128 includes static information relating to thepredetermined area of the object detection system 114, and a grid system132 plotted onto the predetermined area. The grid system 132 is definedby a plurality of grid cells 134. The map definition 128 is directedtowards providing comprehensive environmental information concerning thepredetermined area that does not change frequently. For example, the mapdefinition 128 may include information relating to the location andorientation of the infrastructure located within the predetermined area;the types of traffic signs and signals such as crosswalk signs, yieldsigns, and the like; building height, elevation, orientation as well asother environmental data. The object detection system 114 may generate amap definition 128 using collected sensor information or a mapdefinition 128 may be provided to the object detection system 114.

The data transferring system 110 further includes a series of overlays130. Each of the series of overlays 130 includes a grid system 132.Preferably, the grid system 132 is identical to the grid system 132provided on the map definition 128 so as to reduce processing timeassociated with correlating the two grid systems 132. The grid system132 is plotted over the predetermined area covered by the objectdetection system 114. The overlays 130 include dynamic informationrelating to detected objects within the predetermined range.Specifically, the plotting circuit 124 plots the predicted location ofeach of the detected objects onto the grid system 132 of each of theseries of overlays 130.

The map definition 128 and the overlays 130 may include otherinformation to provide static information relating to the environment ofthe predetermined area and dynamic information relating to the state ofa detected object in a future. For instance the signal phase and timingof traffic lights (SPAT) may be sent to the object detection system 114and utilized in generating both the map definition 128 and the series ofoverlays 130. SPAT information may be used to provide the map definition128 with information relating to the operation of traffic signals withinthe predetermined area. SPAT information may also be used to predict thelocation of detected objects in the predetermined area. Specifically,SPAT information such as the timing of traffic lights may be used in amathematical model to help predict the location of the detected objects.

The path predicting circuit 122 predicts the path of the detectedobjects which may include the path of the system vehicle 138. Any methodof path prediction currently known and used in the art may be adaptablefor use in the path predicting circuit 122. For instance, the pathpredicting circuit 122 may generate a path prediction by plotting thevelocity and location of the detected object so as to create a kinematicvector of each detected object, including the system vehicle 138. In yetanother example, the path predicting circuit 122 uses a statisticalmethod or logical model for predicting the location of detected objectsat a given time.

The data transferring system 110 transmits a cycle of data 126 to thecollision processing circuit 116. The cycle of data 126 includes a firsttransmission of the map definition 128, and subsequent transmissions ofthe overlays 130. The map definition 128 is transmitted at an initialtime T₀. The initial time of transmission may be when the system vehicle138 enters into the predetermined area of the object detection system114. In addition, other factors may trigger the initial time oftransmission. For instance, the object detection system 114 may beprogrammed to preclude transmitting cycles of data 126 when there are noobjects in the predetermined area other than the system vehicle 138.However, the object detection system 114 may transmit the map definition128 at an initial time should the object detection system 114 detectanother obstacle entering into the predetermined area.

Each overlay in the cycle of data 126 is plotted so as to identify thepredicted location of a detected object at T_(0+i*n), where “0” is thetime at which the map definition 128 is transmitted, “i” is the intervalby which path prediction is generated, and “n” is the number of overlays130 generated in a cycle of data 126. For example, assume the datatransferring system 110 is configured to provide path prediction at 0.2second intervals after the initial time, and generates four overlays 130in a cycle of data 126. The first overlay is plotted with the predictedlocation of detected objects at 0.2 seconds after the map definition 128has been transmitted. The second overlay is plotted with the predictedlocation of detected objects at 0.4 seconds after the map definition 128has been transmitted, and so on until four overlays 130 have beengenerated. The overlays 130 may be transmitted separately or bundledtogether with the map definition 128.

The configurable interval in which each of the series of overlays 130 istransmitted may be influenced by factors such as the speed at which thesystem vehicle 138 is operating, the number of detected objects withinthe predetermined area, and the like. For example, if the system vehicle138 and the detected objects are traveling at a speed of less thantwenty miles per hour, the interval by which the overlays 130 aregenerated may be greater than if the system vehicle 138 and detectedobject are traveling at a speed greater than twenty miles per hour.

In another example, the interval at which the overlays 130 are generatedmay be shortened even further if there are more than three detectedobjects within the predetermined area and at least one of those detectedobjects is within a predetermined distance to the system vehicle 138.Another factor that could affect the interval in which the overlays 130are generated is the geographic size of the predetermined area ofcoverage. Thus, if the predetermined area of coverage is five hundredsquare feet, the overlays 130 may be generated at an interval of 0.2seconds whereas if the predetermined area of coverage is one thousandsquare feet, the interval at which each of the overlays 130 is generatedis 0.3 seconds. Likewise, the number of overlays 130 generated is alsoinfluenced by environmental factors. For instance, the number ofoverlays 130 desired may be influenced by the speed of the systemvehicle 138 and the detected objects as well as the geographic size ofthe predetermined area of coverage.

This flexibility allows the data transferring system 110 to be tunable,meaning the data transferring system 110 can generate overlays 130 basedupon the needs of the system vehicle 138. The needs of the systemvehicle 138 may be influenced by factors such as the size of thepredetermined area, the speed of the objects detected within thepredetermined area, and the speed at which the system vehicle 138 istraveling. For instance, where the speed limit of the geographiclocation is thirty five miles per hour and the road is a two-lane road,it may be desirable to predict collisions for periods which occur threeseconds after the system vehicle 138 has entered into the predeterminedarea. Thus, the frequency at which the overlays 130 are generated may belesser than if the geographic area speed limit was fifty miles per hour.Likewise, the number of overlays 130 generated might be less in an areawhere the speed limit is thirty-five miles per hour as opposed to anarea where the speed limit is fifty miles per hour.

It should be appreciated that in some embodiments, it may be preferablefor fielded systems to be specifically configured for a giveninstallation. In such cases, the system is provided with fixed intervals(vs. configurable or dynamically selected intervals) and fixed datarates (vs. variable data rates). However, some self-configuring may bedone (e.g. adjustments/balancing of video and thermal sensor inputs).

After the cycle of data 126 is generated, the data transferring system110 may then transmit the cycle of data 126 to a collision processingcircuit 116. The data transferring system 110 may generate and transmitmultiple cycles of data 126 to the collision processing circuit 116. Thenumber of cycles of data 126 generated may be influenced by such factorsas the presence of the system vehicle 138 within the predetermined areaof coverage, thus ensuring that the system vehicle 138 is provided witha collision warning while in the predetermined area. After a collisionprocessing circuit 16 has received the first cycle of data 126 from theobject detection system 114, subsequent cycles of data 126 may belimited to just a transmission of overlays 130 so as to further reducethe size of data transfer. This is preferable since the map definition128 of a predetermined area may not change significantly while thesystem vehicle is within the predetermined area. Accordingly, asubsequent cycle of data 126 may include a map definition 128 when theenvironmental information relating to the predetermined area of coverageof the object detection system 114 has changed.

It should be appreciated that when a vehicle enters into range of a PWS,a PWS map is needed. Thus, the system periodically broadcasts a mapdefinition (e.g. every one second), while the IOM set is typically sentat a higher frequency (e.g. 5 times per second). The map definition alsoincludes a version number. Use of a version number allows a receiver tonot process the available data if they already have the definition.

The collision processing circuit 116 may be housed within the objectdetection system 114, the system vehicle 138, or offsite. The collisionprocessing circuit 116 processes the cycle of data 126 to determine aprobability of a collision. The collision processing circuit 116 is incommunication with a warning system 136, and actuates the warning system136 if the collision processing circuit 116 determines that theprobability of collision exceeds a predetermined threshold value. Itshould be understood that multiple types of collisions of interest canbe configured for detection by the system (e.g. pedestrian-vehicle orvehicle-vehicle) and thus it is possible to have different thresholdvalues for different types of collisions.

The warning system 136 may be housed in the system vehicle 138 or theobject detection system 114. Any warning system 136 currently known andused in the art is adaptable for use herein, illustratively including adigital display mounted on the dashboard of a system vehicle 138, alight mounted to a post located in the predetermined area operable toflash when a potential collision exists, or a device such as a speakeroperable to send an audible warning to people within the predeterminedarea.

Referring now to FIGS. 12-12B in which like elements are provided havinglike reference designations throughout the several views, a vehiclewhich is five (5) meters long 150 is travelling in a direction indicatedby reference numeral 152 at a speed of four and one-half (4.5)meters/sec. It should be appreciated that FIG. 12A represents a point intime which is two (2) seconds later than the time represented in FIG. 12and that FIG. 12B represents a point in time which is two (2) secondslater than the time represented in FIG. 12A. In each of FIGS. 12-12B,time t₀ represents the present time for that particular figure.

In FIG. 12, the vehicle 150 is shown at a present position in timedesignated as t₀. FIG. 12 also shows vehicle 150 at three future pointsin time at two second intervals (with the future times denoted as t₊₂,t₊₄, t₊₆). Since discrete time computations are used to compute thevehicle locations, a space or gap 154 exists between each of the timeintervals t₀−t₊₆. Given the speed of travel and length of vehicle 150,the gap 154 which exists between each interval can be determined. Thus,in this particular example (i.e. a vehicle 150 which is five (5) meterslong travelling at a speed of four and one-half (4.5) meters/sec), thegap 154 which exists between each two second interval is four (4) metersin length. Also, at this particular speed, the gaps 154 align. This gapalignment phenomenon is sometimes referred to as “track gapping.” Thus,a “track gap” (or more simply a “gap”) can be thought of as a spacebetween where a vehicle location, for example, is computed at a presenttime (e.g. time t_(n)) and at some future point in time (e.g. at a timetwo seconds in the future denoted as time t₊₂). If a pedestrian'spredicted future path is positioned in the gap and the gaps align giventhe speed of the vehicle, then it may not be possible to preciselypredict a collision between the car and the pedestrian. Usingconventional techniques to determine the car speed and gap length, it ispossible to assign an artificial length to the car so that gaps areeliminated. For example, assuming a car has an actual length of fifteenfeet, if the gap is determined to be twenty feet given the speed of thevehicle, then by artificially (mathematically) expanding the length ofthe car from fifteen feet to thirty-five feet, the gap is eliminated.

Thus, as illustrated in FIG. 12B, in such a scenario, a pedestrian 156in a gap 154 may not trigger a collision warning in a system whichutilizes discrete time computations (e.g. traditional Kalman filteringmethods).

The concepts and techniques described herein above, however, provideaccurate solutions for multiple time-phased predictions.

The multi-time predictions provided via the concepts and techniquesdescribed herein in conjunction with FIGS. 1-11 can be chained togetherso as to eliminate the problem of “track gapping” experienced withdiscrete time solutions.

Referring now to FIG. 13, a pedestrian 160 moving along a path 162 (e.g.a sidewalk) reaches an intersection of two streets 164, 166. Pedestrian160 has the option of crossing street 164 (e.g. in a crosswalk 168) orturning left and staying on path 162. Significantly, a crosswalk sign170 indicates to pedestrian 160 that it is safe to cross street 164 bytravelling in cross-walk 168. Thus, in this scenario POM 172 shown inFIG. 13A, may apply.

Referring now to FIG. 13A, exemplary non-continuous POM distribution 172indicates that the probability of pedestrian 160 moving toward crosswalk168 is only slightly greater than the probability of pedestrian 160following sidewalk 162, with only a very small likelihood that thepedestrian will stray off of these two primary paths.

Referring now to FIG. 13B, in which like elements of FIG. 13 areprovided having like reference designations, in this scenario, crosswalksign 170 indicates to pedestrian 160 that it is not safe to cross street164 by travelling in cross-walk 168. Thus, in this scenario POM 174shown in FIG. 13C, will more likely apply.

Referring now to FIG. 13C, exemplary non-continuous POM distribution 174indicates that the probability of pedestrian 160 moving toward crosswalk168 is much less than the probability of pedestrian 160 followingsidewalk 162. In this example, the change in the crosswalk sign 170 isthe significant factor in this change in probability values in POM 172and POM 174. The ability of the POM methodology to readily account forexternal influences/factors (e.g. the state of a crosswalk sign) andaccount for non-continuous probability distributions (e.g. multipleprimary prediction paths with intervening low probability areas) areadvantages over classical future state prediction methods (e.g. Kalmanfilters).

Referring now to FIG. 14, in one embodiment, a universal pedestrianwarning system (UPWS) may includes a computation-less collisiondetection methodology. In this technique, a collision determinationpattern vector (CDPV) is introduced. The CPVD includes an objectclassification (OC) group. Cars and trucks may be grouped together aslike “threats” in the CDPV. In one embodiment sixteen (16) objectclassification result types (pedestrian, vehicle, etc.) are grouped intofour (4) groupings for collision determination. Cell “i” bits refers tothe CDPV bits associated with IOM cell # i.

For each greater-than-zero probability in IOM cells (denoted “i”), thevalue of the CDPV cell (denoted CDPV(i*)) is set equal to one (1) andall other CPDV(i*) cell values are set equal to zero. The number of setsof bits in the CDPV is equal to the number of cells in the IOM. FIGS.14, 14A show that two (2) bits are used for each OC group, and there arefour (4) OC groups. Thus, in this exemplary embodiment, eight (8) bitsare used for each IOM cell i.

Next, a search or “hash” of CDPV for collisions of interest isperformed. In the top row of FIG. 14, 0x05(hexadecimal value 5=bits 0000 01 01) is a pedestrian vs. vehicle collision to be checked. Thesystem can easily be configured to detect other collisions of interest.For example, in the bottom row of FIG. 14, the 0x08 bit being set(hexadecimal value 8=bits 00 00 10 00) identifies a vehicle vs. vehiclecollision to be checked. While the PWS may be deployed to detectpedestrian-vehicle collisions as a primary goal, the flexible,configurable architecture supports reconfiguring the system to alsodetect vehicle-vehicle collisions to provide cooperative intersectioncollision avoidance system (CICAS) capability. Thus, the system findsapplication in the Intelligent Transportation Safety arena.

It has been recognized that CPDV provides the ability to detectcollisions within intersection occupancy map (IOM) data withoutperforming any collision-specific calculations. This uniquecomputation-less collision detection makes use of the CDPV datastructure. As the IOM is populated with future projected objectlocations, a bit is set in the CDPV based upon each object'sclassification type. A plurality of bits, here two bits, are used foreach object classification type. Potentially, more that two bits can beused, but in applications in which a goal is to minimize memory to besearched, two bits is preferred to reflect the necessary information.The CDPV reflects if zero, one or more objects of any grouping ispredicted in a IOM cell at a future time (with any non-zeroprobability). A multiple vehicle-multiple pedestrian scenario would berepresented in the CDPV by 0x0a (hexadecimal value a=bits 00 00 10 10).

A rapid search (e.g. a “hash”) of the CDPV identifies the cell(s)containing configured collisions of interest (i.e. specific bit patternscontained within the cells are found). The corresponding cell IOM datais then further checked for collision alert thresholds.

Having described preferred embodiments which serve to illustrate variousconcepts, structures and techniques which are the subject of thispatent, it will now become apparent to those of ordinary skill in theart that other embodiments incorporating these concepts, structures andtechniques may be used. Accordingly, it is submitted that that scope ofthe patent should not be limited to the described embodiments but rathershould be limited only by the spirit and scope of the following claims.

What is claimed is:
 1. A system for predicting the path of a pedestriancomprising: a local area abstraction (LAA) grid; a pedestrian predictionlogic (PPL) processor which receives a geographical map or image andoperates on object data wherein human behavior, pedestrian behavior orother non-strictly-Kinematic motion may be present to populate the LAAgrid; and a first pedestrian occupancy map (POM) overlaying the LAAgrid, said POM including a probability distribution of future pedestrianan occupancy within the POM.
 2. The system of claim 1 wherein the PPLprocessor provides probabilistic object information to a pedestrianwarning system (PWS).
 3. The system of claim 2 wherein the probabilitydistribution for an individual pedestrian is based upon one or moreparameters related to a pedestrian or an actor being modeled, includingbut not limited to positional location, speed, direction and size, saidPPL computes probabilistic values and stores the values in said firstPOM.
 4. The system of claim 1 wherein said first POM is a first one of aplurality of POMs, each of said plurality of POMs having a probabilitydistribution corresponding to an individual pedestrian or othernon-strictly-Kinematic actor.
 5. The system of claim 4 wherein a singleindividual selected-POM is identified by performing a look-up or otherprocedure to determine the best/correct POM representation for aspecific pedestrian or actor.
 6. The system of claim 5 wherein for eachPOM chosen to model a current or future probable location of a subjectpedestrian or actor, the probability distribution of the individual POMis overlaid onto and added to an existing LAA.
 7. The system of claim 6wherein the LAA corresponds to an intersection occupancy map (IOM). 8.The system of claim 7 wherein the POM corresponds to one of: a pin POM(PPOM); or a cell POM (CPOM).
 9. The system of claim 7 wherein the POMcorresponds to a pin POM (PPOM) which assumes that the probabilitiesdefined by the POM occur at some finite number of distinct points, eachdefined to be a specific distance and bearing from a reference positionand a reference heading of the PPOM.
 10. The system of claim 7 whereinthe POM corresponds to a cell POM (CPOM) which assumes that theprobabilities defined by the POM occur within some finite number ofdistinct cells.
 11. The system of claim 7 wherein the POM is providedhaving a shape corresponding to one of: a rectangular shape; a squareshape; an oval shape; a round shape; a triangular shape; a polygonalshape; a convex polygonal shape; a non-convex polygonal shape; anon-polygonal shape; a convex non-polygonal shape; and a non-convexnon-polygonal shape.